Potential of visible/near-infrared hyperspectral imaging for rapid detection of freshness in unfrozen and frozen prawns

• Hyperspectral imaging technique was used to differentiate freshness of prawns.
• The detection of less freshness adulteration in prawns was investigated.
• An algorithm for automotive selecting the region of interest was developed.
• Three classification models based on LS-SVM, AdaBoost, and ANN were established.
• An image processing algorithm for visualizing classification was developed.

The potential of visible and near infrared (400–1000 nm) hyperspectral imaging as a rapid and non-invasive method was investigated to differentiate freshness of prawns. In both unfrozen and frozen groups (a total of 280 prawns), two different freshness levels were used for classification, respectively. Mean spectral data from the full surface of prawns were extracted automatically as the hyperspectral cubes. Both the first and second derivative spectra were performed for waveform analysis. Successive projections algorithm (SPA) was conducted to select the individual feature wavelengths for classification. Least squares-support vector machine (LS-SVM), adaptive boosting (AdaBoost) algorithm and back-propagation neutral networks (BP-NN) were carried out for classification using the derivative spectrums based on both full wavelengths and selected feature wavelengths. The results demonstrated that SPA–LS-SVM achieved satisfactory average correct classification rate of 98.33% and 95% for prediction samples in unfrozen and frozen groups, respectively. Visualization map of classification of eight prawns (two groups) was also presented. The overall results revealed that hyperspectral imaging technique is promising for freshness classification of prawns rapidly and non-invasively.